trait Distribution extends AnyRef
A distribution of values observed by an instrument. All Kamon distributions are based on the HdrHistogram and as such, they represent a distribution of values within a configured range and precision. By default, all instruments that generate distributions are configured to accept 1% error margin, meaning that all recorded values are adjusted to the bucket whose value is up to 1% away from the original value.
Distributions only expose data for non-empty buckets/percentiles.
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abstract
def
buckets: Seq[Bucket]
Returns an immutable list of all buckets on this distribution.
Returns an immutable list of all buckets on this distribution. Calling this function requires allocation of new bucket instances every time it is called, if you just need to read the values to transfer them into another medium consider using a buckets iterator instead.
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abstract
def
bucketsIterator: Iterator[Bucket]
Returns an iterator of all buckets on this distribution.
Returns an iterator of all buckets on this distribution. This iterator uses a mutable buckets instance that gets updated as the user iterates through them to avoid allocating intermediary objects and thus, trying to copy bucket instances from the iterator into other structures might not work as you expect.
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abstract
def
count: Long
Number of values stored in this distribution.
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abstract
def
dynamicRange: DynamicRange
Describes the range and precision settings of the instrument from which this distribution was taken.
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abstract
def
max: Long
Maximum value in this distribution.
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abstract
def
min: Long
Minimum value in this distribution.
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abstract
def
percentile(rank: Double): Percentile
Returns the percentile at the specified rank.
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abstract
def
percentiles: Seq[Percentile]
Returns an immutable list of all percentiles on this distribution.
Returns an immutable list of all percentiles on this distribution. Calling this function requires allocation of new percentile instances every time it is called, if you just need to read the values to transfer them into another medium consider using a percentiles iterator instead.
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abstract
def
percentilesIterator: Iterator[Percentile]
Returns an iterator of all percentile values on this distribution.
Returns an iterator of all percentile values on this distribution. This iterator uses a mutable percentiles instance that gets updated as the user iterates through them to avoid allocating intermediary objects and thus, trying to copy percentile instances from the iterator into other structures might not work as you expect.
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abstract
def
sum: Long
Sum of all values in this distribution.
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